An efficient framework for improving accuracy in sports analysis using logistic regression algorithm compared with naive bayes algorithm
To improve the precision level of sports examination utilizing the Logistic Regression calculation and its exhibition is contrasted with the Naive Bayes calculation. Sports investigation was finished utilizing Naive Bayes with test size=10 and Logistic Regression calculation with test size=10 with 9...
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| creator | Rajesh, Y. Sindhu, G. |
| description | To improve the precision level of sports examination utilizing the Logistic Regression calculation and its exhibition is contrasted with the Naive Bayes calculation. Sports investigation was finished utilizing Naive Bayes with test size=10 and Logistic Regression calculation with test size=10 with 95% certainty span and pretest force of 80% was iterated at various times for anticipating the precision level of sports treatment of Football. The Logistic Regression calculation (91.87%) seemed to perform better compared to the Naive Bayes calculation (76.23%). The factual importance contrast 0.01 (p |
| doi_str_mv | 10.1063/5.0177011 |
| format | Conference Proceeding |
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Sports investigation was finished utilizing Naive Bayes with test size=10 and Logistic Regression calculation with test size=10 with 95% certainty span and pretest force of 80% was iterated at various times for anticipating the precision level of sports treatment of Football. The Logistic Regression calculation (91.87%) seemed to perform better compared to the Naive Bayes calculation (76.23%). The factual importance contrast 0.01 (p<0.05 Independent Sample T-Test) esteem expresses that the outcomes in the review are critical. Calculated Regression calculation demonstrates with better anticipating exactness rate for projection of sports examination of Football.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0177011</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Algorithms ; Football ; Mathematical analysis ; Regression</subject><ispartof>AIP conference proceedings, 2023, Vol.2821 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). 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Sports investigation was finished utilizing Naive Bayes with test size=10 and Logistic Regression calculation with test size=10 with 95% certainty span and pretest force of 80% was iterated at various times for anticipating the precision level of sports treatment of Football. The Logistic Regression calculation (91.87%) seemed to perform better compared to the Naive Bayes calculation (76.23%). The factual importance contrast 0.01 (p<0.05 Independent Sample T-Test) esteem expresses that the outcomes in the review are critical. 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Sports investigation was finished utilizing Naive Bayes with test size=10 and Logistic Regression calculation with test size=10 with 95% certainty span and pretest force of 80% was iterated at various times for anticipating the precision level of sports treatment of Football. The Logistic Regression calculation (91.87%) seemed to perform better compared to the Naive Bayes calculation (76.23%). The factual importance contrast 0.01 (p<0.05 Independent Sample T-Test) esteem expresses that the outcomes in the review are critical. Calculated Regression calculation demonstrates with better anticipating exactness rate for projection of sports examination of Football.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0177011</doi><tpages>7</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
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| issn | 0094-243X 1551-7616 |
| language | eng |
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| source | AIP Journals Complete |
| subjects | Algorithms Football Mathematical analysis Regression |
| title | An efficient framework for improving accuracy in sports analysis using logistic regression algorithm compared with naive bayes algorithm |
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